title: Approximation-based common principal component for feature extraction in multi-class brain-computer interfaces.
abstract: Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.
authors.forename: Tuan
authors.initials: T
authors.lastname: Hoang
authors.forename: Dat
authors.initials: D
authors.lastname: Tran
authors.forename: Xu
authors.initials: X
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D025341:Principal Component Analysis; D060388:Support Vector Machine